Articles | Volume 35, issue 1
https://doi.org/10.5194/ejm-35-45-2023
https://doi.org/10.5194/ejm-35-45-2023
Research article
 | 
17 Jan 2023
Research article |  | 17 Jan 2023

Shear properties of MgO inferred using neural networks

Ashim Rijal, Laura Cobden, Jeannot Trampert, Hauke Marquardt, and Jennifer M. Jackson

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Cited articles

Bishop, C. M.: Mixture density networks, Tech. rep., Aston University, Birmingham, https://publications.aston.ac.uk/id/eprint/373/1/NCRG_94_004.pdf (last access: 20 December 2022), 1994. a, b, c
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Calandrini, E., Paulatto, L., Antonangeli, D., He, F., Lobo, R. P. S. M., Capitani, F., Brubach, J.-B., Roy, P., Vincent, L., and Giura, P.: Limits of the quasiharmonic approximation in MgO: Volume dependence of optical modes investigated by infrared reflectivity and ab initio calculations, Phys. Rev. B, 103, 054302, https://doi.org/10.1103/PhysRevB.103.054302, 2021. a
Cammarano, F., Deuss, A., Goes, S., and Giardini, D.: One-dimensional physical reference models for the upper mantle and transition zone: Combining seismic and mineral physics constraints, J. Geophys. Res.-Sol. Ea., 110, B01306, https://doi.org/10.1029/2004JB003272, 2005. a
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Short summary
Using neural networks with experimental data, we infer the relationship between pressure, temperature and shear properties of MgO. Fixing the form of the relationship, which is a common practice, provides the properties that are largely constrained by the form and not the data. Our approach provides realistic uncertainties in shear properties, which should improve uncertainty quantification in interpretations of observed shear wave speed to infer the structure and dynamics of the Earth’s mantle.